Adversarial Attacks to Machine Learning-Based Smart Healthcare Systems

Newaz, AKM Iqtidar; Haque, Nur Imtiazul; Sikder, Amit Kumar; Rahman, Mohammad Ashiqur; Uluagac, A. Selcuk

doi:10.48550/arxiv.2010.03671

Cited by 4 publications

(3 citation statements)

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“…Newaz et al [61] investigated adversarial attacks to machine learning-based smart healthcare systems, consisting of 10 vital signs, e.g., EEG, ECG, SpO 2 , respiration, blood pressure, blood glucose, blood hemoglobin, etc. They performed both targeted and non-targeted attacks, and both poisoning and evasion attacks.…”

Section: B Adversarial Attacks In Health Informaticsmentioning

confidence: 99%

Adversarial Attacks and Defenses in Physiological Computing: A Systematic Review

Wu¹,

Fu²,

Zhang³

et al. 2021

Preprint

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Physiological computing uses human physiological data as system inputs in real time. It includes, or significantly overlaps with, brain-computer interfaces, affective computing, adaptive automation, health informatics, and physiological signal based biometrics. Physiological computing increases the communication bandwidth from the user to the computer, but is also subject to various types of adversarial attacks, in which the attacker deliberately manipulates the training and/or test examples to hijack the machine learning algorithm output, leading to possibly user confusion, frustration, injury, or even death. However, the vulnerability of physiological computing systems has not been paid enough attention to, and there does not exist a comprehensive review on adversarial attacks to it. This paper fills this gap, by providing a systematic review on the main research areas of physiological computing, different types of adversarial attacks and their applications to physiological computing, and the corresponding defense strategies. We hope this review will attract more research interests on the vulnerability of physiological computing systems, and more importantly, defense strategies to make them more secure.

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Section: B Adversarial Attacks In Health Informaticsmentioning

confidence: 99%

Adversarial Attacks and Defenses in Physiological Computing: A Systematic Review

Wu¹,

Fu²,

Zhang³

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…In addition, leveraging a man-in-the-middle (MitM) attack in the open communication channel of a BIoT-based model, an attacker can sniff and tamper sensor measurements. Newaz et al demonstrated that adversarial machine learning based attack generation technique can be adopted for compromising sensor measurements without alarming the system [19]. As the sensor measurements of the BIoT system can be altered due to adversarial intent or sensor faults, the controller undertakes several security measures.…”

Section: E Biot Hvac Control Attack Modelmentioning

confidence: 99%

BIoTA Control-Aware Attack Analytics for Building Internet of Things

Haque¹,

Rahman²,

Chen³

et al. 2021

Preprint

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Modern building control systems adopt demand control heating, ventilation, and cooling (HVAC) for increased energy efficiency. The integration of the Internet of Things (IoT) in the building control system can determine real-time demand, which has made the buildings smarter, reliable, and efficient. As occupants in a building are the main source of continuous heat and CO2 generation, estimating the accurate number of people in real-time using building IoT (BIoT) system facilities is essential for optimal energy consumption and occupants' comfort. However, the incorporation of less secured IoT sensor nodes and open communication network in the building control system eventually increases the number of vulnerable points to be compromised. Exploiting these vulnerabilities, attackers can manipulate the controller with false sensor measurements and disrupt the system's consistency. The attackers with the knowledge of overall system topology and control logics can launch attacks without alarming the system. This paper proposes a building internet of things analyzer (BIoTA) framework 1 that assesses the smart building HVAC control system's security using formal attack modeling. We evaluate the proposed attack analyzer's effectiveness on the commercial occupancy dataset (COD) and the KTH live-in lab dataset. To the best of our knowledge, this is the first research attempt to formally model a BIoT-based HVAC control system and perform an attack analysis.

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“…However, it is highly susceptible to data poisoning attacks [115], [116], and in some settings, gain greater degrees of reliability so that they are hard to be identified [117], [118]. In a very sensitive field of study, an experiment on around 17,000 records of healthy, unhealthy (disease-infected) people, a poisoning attack on the training data was able to drop the classifier accuracy of about 28% of its original accuracy by poising 30% of the data [104]. This could have severe consequences, for example, on dosage or treatment management.…”

Section: Food Safetymentioning

confidence: 99%

Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical Challenges

Ahmad¹,

Maabreh²,

Ghaly³

et al. 2020

Preprint

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As we make tremendous advances in machine learning and artificial intelligence (ML/AI) technosciences, there is a renewed understanding in the ML/AI community that we must ensure that humans being are at the center of our deliberations so that we don't end in technology-induced dystopias. As strongly argued by Green in his book "Smart Enough City", the incorporation of technology in city environs does not automatically translate into prosperity, wellbeing, urban livability, or social justice. There is a great need to deliberate on the future of the cities worth living and designing. There are philosophical and ethical questions involved along with various challenges that relate to the security, safety, and interpretability of ML/AI algorithms that will form the technological bedrock of future cities. A number of research institutes on humancentered AI (HAI) have been established at top international universities-including Stanford Institute for Human-Centered Artificial Intelligence (HAI), Berkeley's Center for Human-Compatible AI (CHAI), and MIT Institute for Data, Systems, and Society (IDSS). Globally there are calls for technology to be made more humane and human-compatible. For example, Stuart Russell has a book called Human-Compatible AI. The Center for Humane Technology advocates for regulators and technology companies to avoid business models and product features that contribute to social problems such as extremism, polarization, misinformation, and Internet addiction. In this paper, we analyze and explore key challenges including security, robustness, interpretability, and ethical (data and algorithmic) challenges to a successful deployment of AI/ML in human-centric applications, with a particular emphasis on the convergence of these concepts/challenges. We provide a detailed review of existing literature on these key challenges and analyze how one of these challenges may lead to others or help in solving other challenges. The paper also advises on the current limitations, pitfalls, and future directions of research in these domains, and how it can fill the current gaps and lead to better solutions. We believe such rigorous analysis will provide a baseline for future research in the domain.

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Adversarial Attacks to Machine Learning-Based Smart Healthcare Systems

Cited by 4 publications

References 0 publications

Adversarial Attacks and Defenses in Physiological Computing: A Systematic Review

Adversarial Attacks and Defenses in Physiological Computing: A Systematic Review

BIoTA Control-Aware Attack Analytics for Building Internet of Things

Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical Challenges

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